Cooperative Multi-Satellite ISAC Networks: Centralized vs. Distributed Sensing

Abstract

This paper investigates a downlink multi-satellite integrated sensing and communication (ISAC) network, in which multiple satellites simultaneously transmit ISAC signals to provide communication services to ground user equipments and enable cooperative sensing of airborne targets through multiple gateways. To support this dual functionality, we introduce communication and sensing beamforming designs based on uniform planar arrays with optimized power allocation. Building on these designs, we propose two cooperative sensing frameworks, namely centralized and distributed. In the centralized framework, each gateway forwards its sensing observations to a central unit (CU), where the positions of multiple targets are jointly estimated from the aggregated data. To mitigate the signaling overhead inherent in centralized processing, a distributed framework is further proposed, in which each gateway independently estimates target positions and transmits only the local estimates to the CU. To associate estimates from different gateways, a data association problem based on the squared Euclidean distance is formulated and efficiently solved using the Hungarian algorithm. The final target positions are then obtained by minimizing the distance error. Simulation results demonstrate that the proposed centralized and distributed frameworks significantly outperform existing sensing schemes while satisfying communication performance requirements. Finally, we evaluate the sensing-communication trade-off from the viewpoints of sensing accuracy and communication power consumption under the proposed frameworks.